Modeling alkaline water electrolysis for power-to-x applications: A scheduling approach

The flexible operation of alkaline water electrolyzers enables power-to-x plants to react efficiently to different energy scenarios. In this work, a novel scheduling model for alkaline water electrolysis is formulated as a mixed-integer linear program. The model is constructed by implementing operational states (production, standby, idle) and transitions (cold/full startup, shutdown) as integer variables, while the power loading and hydrogen flowrate are set as continuous variables. The operational characteristics (load range, startup time, ramp rates) are included as model constraints. The proposed model allows finding optimal number of electrolyzers and production schedules when dealing with large data sets of intermittent energy and electricity price. The optimal solution of the case study shows a balance between hydrogen production, energy absorption, and operation and investment costs. The optimal number of electrolyzers to be installed corresponds to 54% of the ones required to absorb the highest energy peak, being capable of loading 89.7% of the available energy during the year of operation, with an overall plant utilization of 93.7% and 764 startup/shutdown cycles evenly distributed among the units.     

Enhanced photoelectrochemical water splitting via engineered surface defects of BiPO4 nanorod photoanodes

Herein, we report on the defect engineering of BiPO₄ nanorods (NRs) via a facile room-temperature template-free co-precipitation method, followed by hydrogen treatment. The hydrogen treatment temperature determined the type of induced defects in the fabricated BiPO₄ NRs and consequently their photocatalytic performance. Upon varying the annealing temperature, the x-ray diffraction (XRD) analysis showed phase transformation and x-ray photoelectron spectroscopy (XPS) analysis revealed variation in the oxygen vacancy content. At moderate treatment temperatures (200–300 °C), shallow defects were predominant, which extended the optical activity of the material to the visible region and increased the photocurrent 3 times when compared to that of bare BiPO₄ NRs. However, treatment at higher temperatures completely altered the crystalline structure, destructed the morphology of the BiPO₄ NRs, and severely affected the photoelectrochemical performance.     

Microseismic zonation maps for Egypt using shear wave velocity (Vs 30), and standard penetration resistance value (N30)

Bulletin of Engineering Geology and the Environment - Although Egypt has not been subjected to high seismic hazards, it does face a very high level of risk on this front. Even a small earthquake of...     

Developing the multiple stress–strain creep recovery (MS-SCR) test

Mechanics of Time-Dependent Materials - While most published work from Europe has been concerned with evaluating binders’ resistance to rutting based on their stiffness (deformation...     

A geometrical-based microcell mobile radio channel model

In this paper we present a geometric multipath propagation model for a microcell mobile environment. The proposed model provides the statistics for the direction-of-arrival (DOA) of multipath components. These statistics are required to test adaptive array algorithms for cellular applications. The proposed model assumes that (1) a line-of-sight (LOS) path exists between the transmitter and the receiver, (2) the scatterers lie within a circle of radius R around the mobile station, and (3) the base station lies within this circle. The distances between the scatterers and the mobile station are subject statistically to a hyperbolic distribution. The model also provides the multipath power delay profiles (PDP), which are used to evaluate the bit error rate (BER) and the signal to interference ratio (SIR) for the direct-sequence code division multiple access (DS-CDMA). We derive and simulate the joint probability density functions (pdfs) of the power—DOA and the power—Doppler shift. Further we determine expressions for the BER performance and for the SIR of a DS-CDMA system over the proposed channel model. A simplified expression based on the improved Gaussian approximation (SEIGA) is used to evaluate the BER and the SIR in a wideband multipath channel. Although the proposed model is applicable for downlink as well, in this paper we will analyze the uplink environment only.     

MoM solutions to building blockage of mobile satellite communications

This article presents a full-wave propagation model for arbitrary profile of building blockage in mobile satellite communications, by solving the electric field integral equation for induced surface currents using the method of moments. Asymptotic expressions are used to simplify the integrals. Scattered fields are then found by the radiation equations derived from Maxwell equations. The total received fields around different profiles of buildings are calculated as a function of space, elevation angle and frequency. The results agree well with measurements and other published data. Various useful parameters for designing robust and reliable communication systems like frequency response, average fade duration and coherence bandwidth are found. Performance of mobile satellite system is evaluated in terms of bit error rate of mobile satellite system in frequency non-selective, slowly fading channel.     

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users’ feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.